“Machine condition monitoring” refers to the accumulation of a wide variety of process parameters and condition parameters related to the machine. Process parameters may include machine speed (e.g., RPM), load, product speed (e.g., items produced, fluid per unit time, etc.), quality, and the like. Condition parameters may include temperature (e.g., at different locations in the machine), exhaust gases (e.g., SOxNOx), oil and grease conditions, particles in the oil and grease, thermography, vibration, ultra sonic sounds, and the like. Together, these process and condition parameters may form a picture of the machine's ability to perform (e.g., efficiency) and the ability to continue to perform (e.g., likelihood of failure).
Determining whether current machine conditions are harmful or whether an ongoing defect exists based upon analysis of the process parameters and/or condition parameters is the domain of a user referred to as a condition monitoring technician. In addition to the condition monitoring technician, many condition monitoring providers also include some form of automatic diagnostic capability in their systems.
One type of automatic diagnostic engine is a model-based engine where measurements, extracted measurement features, and/or how they relate to one another are analyzed to detect specific machine failure conditions. Model-based automatic diagnostic engines tend to fail if the model encounters a set of parameter conditions that is not part of the model's logic. Another form of automatic diagnostic engine is a statistics-based engine where a set of statistical algorithms are analyzed to determine deviation from the norm and/or show outliers. Statistical-based automatic diagnostic engines may not be able to identify what the particular problem is.